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Clinical Trial Details — Status: Active, not recruiting

Administrative data

NCT number NCT05889364
Other study ID # 2018-1
Secondary ID
Status Active, not recruiting
Phase
First received
Last updated
Start date February 1, 2018
Est. completion date December 30, 2023

Study information

Verified date May 2023
Source Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

Testing of AI solutions to assess diagnostic accuracy for tuberculosis detection.


Description:

Tuberculosis remains a key problem of modern medicine. New approaches for burden overcoming should be proposed. New screening strategies may include artificial intelligence (AI). An AI-based system for chest x-ray analysis and triage ("normal/tuberculosis suspected") have been developed and trained. A special data-set was prepared. There are 238 normal x-rays and 70 x-rays with lung tuberculosis in data-set. The data-set was randomly divided into 2 samples: - sample N1 (n=140) with ratio "normal: tuberculosis" 50:50, - sample N1 (n=150) with ratio "normal: tuberculosis" 95:5. Both samples will be analysed by AI-based system. Results will be quantified using diagnostic accuracy metrics: sensitivity and specificity, positive and negative predictor values, likelihood ratio, and area under the ROC (receiver operating characteristic) curve.


Recruitment information / eligibility

Status Active, not recruiting
Enrollment 308
Est. completion date December 30, 2023
Est. primary completion date March 15, 2018
Accepts healthy volunteers
Gender All
Age group 18 Years to 80 Years
Eligibility Inclusion Criteria: - no pathology in a lung on chest x-ray - signs of lung tuberculosis on chest x-ray Exclusion Criteria: - any pathology in the lungs (except tuberculosis)

Study Design


Related Conditions & MeSH terms


Intervention

Diagnostic Test:
AI-based x-ray analysis and triage ("normal/tuberculosis suspected")
All included x-rays will be analysed by the AI-based system. Then results will be compared with opinions of 2 experienced radiologists (they make peer-review of all included images independently of each other).

Locations

Country Name City State
Russian Federation Research and Practical Center of Medical Radiology, Department of Health Care of Moscow Moscow

Sponsors (1)

Lead Sponsor Collaborator
Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

Country where clinical trial is conducted

Russian Federation, 

Outcome

Type Measure Description Time frame Safety issue
Primary Diagnostic accuracy metric 1 Sensitivity Day 1 upon receipt of data
Primary Diagnostic accuracy metric 2 Specificity Day 2 upon receipt of data
Primary Diagnostic accuracy metric 3 Positive predictor values Day 3 upon receipt of data
Primary Diagnostic accuracy metric 4 Negative predictor values Day 4 upon receipt of data
Primary Diagnostic accuracy metric 5 Likelihood ratio Day 5 upon receipt of data
Primary Diagnostic accuracy metric 6 Area under the ROC curve Day 6 upon receipt of data
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